TY  - JOUR
AU  - Zhu, Wenjun
AU  - Zhang, Yingren
AU  - She, Kaijie
AU  - Chen, Jiaxu
PY  - 2026
TI  - Artificial intelligence-driven intelligentization of traditional Chinese medicine diagnosis: Applications, challenges, and prospects of multimodal fusion and large language model
JO  - Journal of Traditional Chinese Medical Sciences
SN  - 2095-7548
SP  - 291
EP  - 301
VL  - 13
IS  - 3
AB  - In the context of the rapid development of artificial intelligence (AI) technology, the intelligentization of traditional Chinese medicine (TCM) diagnosis has become a key research direction for promoting the modernization and internationalization of TCM. TCM diagnosis is based on information from four diagnostic methods, featuring multiple modalities, high dimensions, strong overallness, and significant reliance on experience. It has long faced challenges, such as insufficient objectification, standardization, and repeatability. AI technology has made significant progress in TCM diagnosis, gradually achieving intelligent perception and fusion modeling of multiple sources of diagnostic information and demonstrating good potential in pattern differentiation, reasoning for pattern differentiation, and decision-making for auxiliary diagnosis. Language models provide a novel technical paradigm for the expression, reasoning, and interaction of elements of TCM diagnosis. This article systematically discusses the theoretical basis, key technologies, and application progress of AI in the intelligentization of TCM diagnosis, focusing on the current development status and challenges of multimodal diagnostic modeling, intelligent expression of patterns, and the development of large model-driven diagnostic systems, to provide references for the application and development of intelligent TCM diagnosis.
UR  - https://doi.org/10.1016/j.jtcms.2026.05.002
DO  - 10.1016/j.jtcms.2026.05.002